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A reader-friendly introduction to the exciting, vast potential of Genetic Algorithms. The book gives readers a general understanding of the;
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Genetic Algorithms in Java Basics is a brief introduction to solving problems using genetic algorithms, with working projects and solutions;
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method based on the way chromosomes in DNA recombine - these problems are more efficiently and more accurately solved. Genetic Algorithms for;
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method based on the way chromosomes in DNA recombine - these problems are more efficiently and more accurately solved. Genetic Algorithms for;
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search procedures modeled on the Darwinian concepts of natural selection and evolution. The machinery of genetic algorithms utilizes an;
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an in-depth look at the algorithms, techniques, and methods that go into writing a genetic algorithm. From introductory problems to real;
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World! to optimizing one genetic algorithm with another, and finally genetic programming; thus preparing you to apply genetic algorithms to;
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Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning;
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engineering. Genetic Algorithms and Engineering Design is the only book to cover the most recent technologies and their application to manufacturing;
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planning problem for robot manipulators. Simple and efficient solutions are proposed for the path planning problem based on genetic algorithms. One;
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Computer solutions to many difficult problems in science and engineering require the use of automatic search methods that consider a large;
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Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context;
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Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context;
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Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving;
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, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear;
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, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear;
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, telecommunications, and bioinformatics. Part One-covers methodologies for complex problem solving including genetic programming, neural networks, genetic;
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will enable both students and practitioners to apply genetic algorithms to problems in many fields. Major concepts are illustrated with running;
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Practical Handbook of Genetic Algorithms, Volume 3: Complex Coding Systems contains computer-code examples for the development of genetic;
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Computing? Based on the genetic message encoded in DNA, and digitalized algorithms inspired by the Darwinian framework of evolution by natural;
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Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational;
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Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational;
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The aim of this book is to present state-of-the-art information regarding the practical applications of genetic algorithms. It discusses;
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solving numerous problems in QSAR and drug designWritten to be accessible without prior direct experience in genetic algorithms;
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Foundations of Genetic Algorithms 1991 (FOGA 1) discusses the theoretical foundations of genetic algorithms (GA) and classifier systems;
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Cellular Genetic Algorithms defines a new class of optimization algorithms based on the concepts of structured populations and Genetic;
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Genetic and Evolutionary Methods is a compendium of articles and papers that were presented at GEM '14, an international conference that;
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